Overview

Dataset statistics

Number of variables12
Number of observations2965
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.1 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtd_invoices and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_invoicesHigh correlation
qtd_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtd_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.14464074)Skewed
returns is highly skewed (γ1 = 21.97352154)Skewed
customer_id has unique valuesUnique
recency has 33 (1.1%) zerosZeros
returns has 1480 (49.9%) zerosZeros

Reproduction

Analysis started2023-11-13 17:32:26.542500
Analysis finished2023-11-13 17:33:10.256327
Duration43.71 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2965
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.25
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:10.566219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.2
Q113799
median15220
Q316770
95-th percentile17964.8
Maximum18287
Range5940
Interquartile range (IQR)2971

Descriptive statistics

Standard deviation1719.5227
Coefficient of variation (CV)0.11260606
Kurtosis-1.2063686
Mean15270.25
Median Absolute Deviation (MAD)1489
Skewness0.032497971
Sum45276291
Variance2956758.3
MonotonicityNot monotonic
2023-11-13T14:33:10.939807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2955) 2955
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2950
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2695.7413
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:11.291303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.892
Q1570.96
median1086.92
Q32308.06
95-th percentile7180.164
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10140.316
Coefficient of variation (CV)3.7616057
Kurtosis396.92543
Mean2695.7413
Median Absolute Deviation (MAD)671.72
Skewness17.627256
Sum7992872.9
Variance1.0282601 × 108
MonotonicityNot monotonic
2023-11-13T14:33:11.673208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.02 2
 
0.1%
1353.74 2
 
0.1%
734.94 2
 
0.1%
1025.44 2
 
0.1%
598.2 2
 
0.1%
533.33 2
 
0.1%
731.9 2
 
0.1%
2092.32 2
 
0.1%
379.65 2
 
0.1%
745.06 2
 
0.1%
Other values (2940) 2945
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.214503
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:12.040444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.578787
Coefficient of variation (CV)1.2081194
Kurtosis2.7602371
Mean64.214503
Median Absolute Deviation (MAD)26
Skewness1.7946797
Sum190396
Variance6018.4681
MonotonicityNot monotonic
2023-11-13T14:33:12.469018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.9%
2 84
 
2.8%
8 76
 
2.6%
10 67
 
2.3%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2216
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.9%
4 87
2.9%
5 43
1.5%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtd_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7254637
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:12.859311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8563763
Coefficient of variation (CV)1.54684
Kurtosis190.95926
Mean5.7254637
Median Absolute Deviation (MAD)2
Skewness10.769262
Sum16976
Variance78.435401
MonotonicityNot monotonic
2023-11-13T14:33:13.223350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
1 189
 
6.4%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 189
 
6.4%
2 784
26.4%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qtd_items
Real number (ℝ)

HIGH CORRELATION 

Distinct1671
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1583.4853
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:13.587077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median642
Q31401
95-th percentile4404
Maximum196844
Range196842
Interquartile range (IQR)1104

Descriptive statistics

Standard deviation5708.0046
Coefficient of variation (CV)3.6047095
Kurtosis516.26228
Mean1583.4853
Median Absolute Deviation (MAD)422
Skewness18.729187
Sum4695034
Variance32581317
MonotonicityNot monotonic
2023-11-13T14:33:13.994948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
260 8
 
0.3%
246 8
 
0.3%
84 8
 
0.3%
394 7
 
0.2%
200 7
 
0.2%
Other values (1661) 2882
97.2%
ValueCountFrequency (%)
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

qtd_products
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.85228
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:14.407352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation270.04472
Coefficient of variation (CV)2.1981255
Kurtosis354.52323
Mean122.85228
Median Absolute Deviation (MAD)44
Skewness15.701118
Sum364257
Variance72924.151
MonotonicityNot monotonic
2023-11-13T14:33:14.804921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.5%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
25 31
 
1.0%
27 30
 
1.0%
26 30
 
1.0%
Other values (457) 2625
88.5%
ValueCountFrequency (%)
1 5
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 28
0.9%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2697 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1672 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2963
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.013426
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:15.191359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9135686
Q113.119333
median17.974384
Q324.988286
95-th percentile90.2355
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation119.59119
Coefficient of variation (CV)3.6225016
Kurtosis812.16217
Mean33.013426
Median Absolute Deviation (MAD)5.9942223
Skewness25.144641
Sum97884.807
Variance14302.052
MonotonicityNot monotonic
2023-11-13T14:33:15.554997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
4.162 2
 
0.1%
18.15222222 1
 
< 0.1%
21.47435897 1
 
< 0.1%
3.411945289 1
 
< 0.1%
16.29372093 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
Other values (2953) 2953
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.33252
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:15.907062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.285714
Q385.333333
95-th percentile200.8
Maximum366
Range365
Interquartile range (IQR)59.333333

Descriptive statistics

Standard deviation63.521583
Coefficient of variation (CV)0.94340123
Kurtosis4.9035857
Mean67.33252
Median Absolute Deviation (MAD)26.285714
Skewness2.0656046
Sum199640.92
Variance4034.9915
MonotonicityNot monotonic
2023-11-13T14:33:16.284762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
70 21
 
0.7%
4 21
 
0.7%
7 20
 
0.7%
35 18
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
11 17
 
0.6%
21 17
 
0.6%
5 16
 
0.5%
Other values (1248) 2775
93.6%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 21
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct1348
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063184863
Minimum0.0054495913
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:16.669155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0094339623
Q10.017777778
median0.029304029
Q30.055393586
95-th percentile0.22222222
Maximum3
Range2.9945504
Interquartile range (IQR)0.037615808

Descriptive statistics

Standard deviation0.13442478
Coefficient of variation (CV)2.127484
Kurtosis121.94959
Mean0.063184863
Median Absolute Deviation (MAD)0.014266435
Skewness8.7930514
Sum187.34312
Variance0.018070022
MonotonicityNot monotonic
2023-11-13T14:33:17.046115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 21
 
0.7%
0.1666666667 21
 
0.7%
0.02777777778 20
 
0.7%
0.09090909091 19
 
0.6%
0.0625 17
 
0.6%
0.1333333333 16
 
0.5%
0.4 15
 
0.5%
0.02380952381 15
 
0.5%
0.25 15
 
0.5%
0.03571428571 15
 
0.5%
Other values (1338) 2791
94.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.5 3
 
0.1%
1 14
0.5%
0.8333333333 1
 
< 0.1%
0.75 1
 
< 0.1%
0.6666666667 12
0.4%
0.6514745308 1
 
< 0.1%
0.6 1
 
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.827993
Minimum0
Maximum9014
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:17.426247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile99.6
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.97012
Coefficient of variation (CV)8.1247897
Kurtosis595.93568
Mean34.827993
Median Absolute Deviation (MAD)1
Skewness21.973522
Sum103265
Variance80072.089
MonotonicityNot monotonic
2023-11-13T14:33:17.814663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.5%
8 43
 
1.5%
Other values (203) 704
23.7%
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.5%
8 43
 
1.5%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1978
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.46733
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:18.163882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.277778
Q1103.3
median172.33333
Q3281.69231
95-th percentile599.76
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.39231

Descriptive statistics

Standard deviation283.96574
Coefficient of variation (CV)1.2008667
Kurtosis102.75719
Mean236.46733
Median Absolute Deviation (MAD)83.083333
Skewness7.701917
Sum701125.63
Variance80636.543
MonotonicityNot monotonic
2023-11-13T14:33:18.538142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
136 8
 
0.3%
130 7
 
0.2%
Other values (1968) 2878
97.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct907
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.504034
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-13T14:33:18.920132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.7096774
median13.6
Q322.142857
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.43318

Descriptive statistics

Standard deviation15.465464
Coefficient of variation (CV)0.88353715
Kurtosis29.301065
Mean17.504034
Median Absolute Deviation (MAD)6.6
Skewness3.4352829
Sum51899.46
Variance239.18058
MonotonicityNot monotonic
2023-11-13T14:33:19.304811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
17 38
 
1.3%
14 38
 
1.3%
7 36
 
1.2%
11 36
 
1.2%
5 36
 
1.2%
15 35
 
1.2%
Other values (897) 2585
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-11-13T14:33:06.376448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:27.125066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:30.250100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:34.475258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:40.064292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:43.462112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:46.782652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:50.473675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:53.527535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:56.644313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:59.704608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:03.326046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:06.642495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:27.372643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:30.495719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:34.945601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:40.434623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:43.732287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:47.039281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:50.734426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:53.786777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:56.902620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:59.940570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:03.570243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:06.896945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:27.635763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:30.745174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:35.471873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:40.865227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:44.007168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:47.668596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:50.974688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:54.041110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:57.150425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:00.180656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:03.813734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:07.157589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:27.925421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:31.000315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:35.968731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:41.154269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:44.288825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:47.950679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:51.223888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:54.308713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:57.416731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:00.432692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:04.075831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:07.387663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:28.173310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:31.250540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:36.329627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:41.375489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:44.573129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:48.200249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:51.451647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:54.557060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:57.653451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:00.658663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:04.314871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:07.655919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:28.450702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:31.527248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:36.825571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:41.655759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:44.858477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:48.487937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:51.712343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:54.840676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:57.925974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:00.921012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:04.589834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:07.927398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:28.727032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:32.087114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:37.368314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:41.921130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:45.136165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:48.772856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:51.977717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:55.118029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:58.205809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:01.195256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:04.874698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:08.158029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:28.963370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:32.316813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:37.791980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:42.144729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:45.376457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:49.037584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:52.196558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:55.354659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:58.437885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:01.476942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:05.107588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:08.422140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:29.222399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:32.587233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:38.254665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:42.399984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:45.665345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:49.326839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:52.452481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:55.622478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:58.697457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:01.835059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:05.375062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:08.675468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:29.494030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:32.982513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:38.727113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:42.660428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:45.933676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:49.633680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:52.708725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:55.884874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:58.956131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:02.078022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:05.637038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:08.907032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:29.727556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:33.396030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:39.203841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:42.920190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:46.190923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:49.883730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:52.940981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:56.125890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:59.192590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:02.797859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:05.871866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:09.158568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:29.984146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:33.850007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:39.670013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:43.228473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:46.512003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:50.158094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:53.194062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:56.384296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:32:59.455564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:03.056021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-13T14:33:06.123896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-13T14:33:20.097260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0770.0000.026-0.0710.013-0.1310.018-0.008-0.064-0.124-0.017
gross_revenue-0.0771.000-0.4140.7710.9250.7450.245-0.2500.1630.3720.5730.104
recency0.000-0.4141.000-0.503-0.407-0.4360.0490.109-0.033-0.120-0.0960.015
qtd_invoices0.0260.771-0.5031.0000.7170.6890.060-0.2600.1500.2960.100-0.183
qtd_items-0.0710.925-0.4070.7171.0000.7310.166-0.2300.1480.3440.7290.146
qtd_products0.0130.745-0.4360.6890.7311.000-0.378-0.1660.1010.2450.3830.515
avg_ticket-0.1310.2450.0490.0600.166-0.3781.000-0.1230.1000.1900.186-0.619
avg_recency_days0.018-0.2500.109-0.260-0.230-0.166-0.1231.000-0.962-0.399-0.0800.130
frequency-0.0080.163-0.0330.1500.1480.1010.100-0.9621.0000.3610.059-0.103
returns-0.0640.372-0.1200.2960.3440.2450.190-0.3990.3611.0000.210-0.053
avg_basket_size-0.1240.573-0.0960.1000.7290.3830.186-0.0800.0590.2101.0000.403
avg_unique_basket_size-0.0170.1040.015-0.1830.1460.515-0.6190.130-0.103-0.0530.4031.000

Missing values

2023-11-13T14:33:09.564943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-13T14:33:10.061382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.0297.018.15222235.5000000.48611140.050.9705880.617647
1130473232.5956.09.01390.0171.018.90403527.2500000.04878035.0154.44444411.666667
2125836705.382.015.05028.0232.028.90250023.1875000.04569950.0335.2000007.600000
313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
415100876.00333.03.080.03.0292.0000008.6000000.13636422.026.6666670.333333
5152914623.3025.014.02102.0102.045.32647123.2000000.05444129.0150.1428574.357143
6146885630.877.021.03621.0327.017.21978618.3000000.073569399.0172.4285717.047619
7178095411.9116.012.02057.061.088.71983635.7000000.03910641.0171.4166673.833333
81531160767.900.091.038194.02379.025.5434644.1444440.315508474.0419.7142866.230769
9160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
5601177271060.2515.01.0645.066.016.0643946.00.2857146.0645.00000066.000000
561117232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
561217468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
562313596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
5629148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
563312479473.2011.01.0382.030.015.7733334.00.33333334.0382.00000030.000000
565414126706.137.03.0508.015.047.0753333.01.00000050.0169.3333334.666667
5660135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
567015060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
568912558269.967.01.0196.011.024.5418186.00.285714196.0196.00000011.000000